• 전기전자학회논문지
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• 제23권4호
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• pp.1393-1399
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• 2019

인터넷의 급속한 성장으로 데이터의 송수신의 편리성과 비용이 들지 않는다는 장점 때문에 매일 수백만 건의 무차별적인 광고성 스팸 문자와 메일이 발송되고 있다. 아직은 스팸 단어나 스팸 번호를 차단하는 방법을 주로 사용하지만, 기계 학습이 떠오름에 따라 스팸을 필터링하는 방법에 대해 다양한 방식으로 활발히 연구되고 있다. 그러나 스팸에서만 등장하는 단어나 패턴은 스팸 필터링 시스템에 의해 걸러지지 않기 위해 지속적으로 변화하고 있기 때문에, 기존 기계 학습 메커니즘으로는 새로운 단어와 패턴을 감지, 적응할 수 없다. 최근 이러한 기존 기계 학습의 한계점을 극복하기 위해 기존의 지식을 활용하여 새로운 지식을 지속적으로 학습하도록 하는 Lifelong Learning(이하 LL)의 개념이 대두되었다. 본 논문에서는 문서 분류에 가장 많이 사용되는 나이브 베이즈와 Lifelong Machine Learning(이하 LLML)의 앙상블 기법을 이용한 스팸 메시지 필터링 방법을 제안한다. 우리는 기존 스팸 필터링 시스템에 가장 많이 사용되는 나이브 베이즈와, LLML 모델 중 ELLA를 적용하여 LL의 성능을 검증한다.

• 통상정보연구
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• 제9권2호
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• pp.103-120
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• 2007

The knowledge based economy requires more and more people to learn new knowledge and skills in a timely and effective manner. These needs and new technology such as computer and Internet are fueling a transition in e-learning. According to specialist's opinion, imagination experience studying is generalized, and learning environment that language barrier by studying, multi-language studying Machine that experience past things that disappear through simulation, and travel area, and experience future changed state disappears is forecasting to come. This is previewing finally that it may become future education that education and IT, element of entertainment is combined. Already, became story that argument for party satellite of e-Learning existence passes one season already. e-Learning is utilized already in all educations that we touch by effectiveness by corporation's competitive power improvement and implement of lifelong education in educational institutions through present e-Learning. It is obvious that when see from our viewpoint which is defining e-Learning by one industry and rear by application to education as well as one new growth power about these, e-Learning industry becomes very important means that can solve dilemma of growth real form. Only, special quality of digital industry that e-Learning is being same with other digital industry and repeat putting out a fire rapidly, and is repeating sudden change that these evolution is not gradual growth of accumulation and improvement of technology that is appearing consider need to. In the meantime, we need to observe about evolution of Information Technology. Because there is some scholars who e-Learning's concept foresees to evolve by u-Learning.(although, a person who see that these concept is not more in marketing terminology by some scholars' opinion is). This u-Learning's concept means e-Learning that take advantage of ubiquitous technology as Ubiquitous-Learning's curtailment speech. Ubiquitous, user means Information-Communication surrounding that can connect to network freely regardless of place without feeling network or computer. There is controversy about introduction time regarding these direction, but e-Learning is judged to evolve by u-Learning necessarily. Because keep in step and age that study all contents that learner wants under environment of 3A (any time, any whrer, any device) by individual order thoroughly is foreseen to come in ubiquitous learning environment that approach more festinately.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.19-30
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• 2021

Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.